Emergent Insight: We all are recipients of the untiring work of artificial intelligence but we don't take time to acknowledge it. Industrial businesses certainly are leveraging the powerful technology as noted in this post by Michael Sharp at Metrology News. In your company tasks and objectives, take a moment to consider how rules-based AI or machine learning can improve productivity, safety or more. Machines, devices and computers usually take over tasks that are mundane and laborious and don't really require a human to do. Why not let AI do the work and switch the human employees to more satisfying roles?
This blog post has been written with the collaboration of Juan Olloniego and Germán Hoffman. Even if machines have done a big part of the heavy lifting for us since the industrial revolution, they still depend on us for their maintenance. As they have that annoying tendency to break from time to time, their conservation becomes essential to keep up with our daily activities. Now, with the industry 4.0, the internet of things, and the artificial intelligence advent, we are letting a new kind of machines take care of their older counterparts. We make these new transistor-based machines look after their ancestors.
The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.
Data Analytics has been the backbone of some of the revolutionary companies that are disrupting the ecosystem. Financial Services is no different. These challenges are picked by our editorial team based on the latest trends and development. Do read it till the end to enhance your understanding of the subject. If you think we are missing anything, let us know in the comments and our team will review and add it into the blog.
Computer assistants and AIs perform an ever-growing range of tasks that are broadly intended to improve our quality of life. This extends to industry as well. But first, what do we mean by artificial intelligence? In simple terms, it's any machine (usually a computer) that does things normally associated with human intelligence, such as reasoning, learning and self-improvement. AI systems in industry are the same technologies you use in daily life but applied to industrial problems.
Today I'm excited to announce Snorkel AI's launch out of stealth! Snorkel AI, which spun out of the Stanford AI Lab in 2019, was founded on two simple premises: first, that the labeled training data machine learning models learn from is increasingly what determines the success or failure of AI applications. And second, that we can do much better than labeling this data entirely by hand. At the Stanford AI lab, the Snorkel AI founding team spent over four years developing new programmatic approaches to labeling, augmenting, structuring, and managing this training data. We were fortunate to develop and deploy early versions of our technology with some of the world's leading organizations like Google, Intel, Apple, Stanford Medicine, resulting in over thirty-six peer-reviewed publications on our findings; innovations in weak supervision modeling, data augmentation, multi-task learning, and more; inclusion in university computer science curriculums; and deployments in popular products and systems that you've likely interacted with in the last few hours.
As more and more industries bring ML use cases to production, the need for consistent practices for managing ML in Production and optimizing ML Lifecycle iteration has grown rapidly. Last year, a few of us partnered with USENIX to drive the first-ever Industry/Academic conference dedicated to the challenges of and innovations in managing ML in Production. OpML 2019 was a great success - bringing together experts, practitioners, engineers, and researchers to discuss the latest and greatest in ML Ops. You can find a summary of OpML 2019 here. This year, due to COVID19, OpML 2020 became a virtual conference with video presentations and open discussions on Slack.
Informatica's latest acquisition extends machine learning capabilities into matching of data entities and schemas. And the acquisition came out of Informatica's first formal partnership effort with a university. The new capabilities will find their ways into Informatica's existing master data management (MDM), enterprise data catalog, privacy, governance, and data integration offerings. The company, GreenBay Technologies, was co-founded by a University of Wisconsin at Madison computer science professor and began operation with ties to the university and its alumni research foundation. GreenBay and Informatica were hardly strangers, as Informatica was the sole investor in the startup.
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Association Rules find all sets of items (itemsets) that have support greater than the minimum support and then using the large itemsets to generate the desired rules that have confidence greater than the minimum confidence. The lift of a rule is the ratio of the observed support to that expected if X and Y were independent. A typical and widely used example of association rules application is market basket analysis.
Machine learning and Artificial intelligence are the new buzz words that are being thrown around more than any other trending technology today. It is starting to reshape how we think about building products. It's time we understood what it is and why it matters. Machine Learning: (ML) is an area of computational science that enables machines (computers) to undertake tasks without being explicitly programmed. The idea behind machine learning is that by training computers to analyze and interpret existing data from prior human interactions, machines are able to find patterns and structures in data.